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Abstract:

Methods for use of EIT. Disclosed are: (1) EIT used to obtain a final
solution to an EIT inverse problem for localizing tissues undergoing
changes in impedance, which is used as a constraint on solving an EEG
source localization inverse problem; (2) EIT used with MREIT, where the
MREIT is used to constrain the solutions to the EIT inverse problem for
the distribution of static tissue impedance; (3) EIT used with MREIT,
where the MREIT is used to constrain the solutions to the EIT inverse
problem for localizing tissues undergoing changes in impedance; and (4)
EIT according to any of (1)-(3) as feedback for modifying at least one of
the location, magnitude, and timing of currents injected for the purpose
of neurostimulation.

Claims:

1. A method for use of EIT, comprising: performing an EIT procedure,
including distributing a plurality of electrodes on a subject's head such
that each electrode makes electrical contact with the head surface at a
respective location that is spaced apart from the location associated
with each other electrode, and performing an EIT current injecting step
by injecting a first set of currents into the head, each of the currents
of said first set being injected by a corresponding two or more of said
electrodes and having a sufficiently small magnitude so as not to provoke
a substantial brain response, either alone or in combination with other
currents of said first set, and, during a time when said first set of
electrodes is injecting one or more of the currents of said first set,
utilizing a plurality of the remaining electrodes to measure respective
first electrical potentials arising in response; performing at least one
of an EEG procedure and an MREIT procedure, using the same electrodes as
used for performing said EIT procedure; wherein, if said EEG procedure is
performed, the method includes measuring, at said remaining electrodes,
respective second electrical potentials resulting from the electrical
activity of a first set of one or more sources in the brain during said
time, obtaining an EIT final solution to an inverse problem for the
distribution of static tissue impedances in the head posed by said first
injected currents and said first electrical potentials, obtaining one or
more iterative EEG solutions to an inverse problem for localizing the
sources of said first set posed by said second electrical potentials, and
constraining one or more of said EEG solutions by said EIT final
solution; and wherein, if said MREIT procedure is performed, the method
further includes performing an MREIT current injecting step that is the
same as said EIT current injecting step, obtaining a magnetic resonance
image of the currents injected in said MREIT current injecting step, and
(c) obtaining one or more iterative EIT solutions to an inverse problem
for the distribution of static tissue impedances in the head posed by
said first set of currents and said first electrical potentials, or (d)
obtaining one or more iterative EIT solutions to an inverse problem for
localizing head tissues undergoing changes in impedance posed by said
first set of currents and said first electrical potentials, and
constraining one or more of said EIT solutions by said magnetic resonance
image.

2. A method for use of EIT, comprising: performing an EIT procedure,
including distributing a plurality of electrodes on a subject's head such
that each electrode makes electrical contact with the head surface at a
respective location that is spaced apart from the location associated
with each other electrode, and performing an EIT current injecting step
by injecting a first set of currents into the head, each of the currents
of said first set being injected by a corresponding two or more of said
electrodes and having a sufficiently small magnitude so as not to provoke
a substantial brain response, either alone or in combination with other
currents of said first set, and, during a time when said two or more
electrodes is injecting one or more of the currents of said first set,
utilizing a plurality of the remaining electrodes to measure respective
first electrical potentials arising in response; performing an EEG
procedure including a step of measuring, at the same said remaining
electrodes, respective second electrical potentials resulting from the
electrical activity of a first set of one or more sources in the brain
occurring during said time; obtaining a final solution to an EIT inverse
problem posed by said first electrical potentials in accordance with said
first first set of currents, for specifying the locations of tissue
within the head undergoing changes in impedance; and obtaining one or
more iterative solutions to an EEG inverse problem posed by said second
electrical potentials for localizing the sources of said first set,
including constraining one or more of said iterative solutions by said
final solution.

3. The method of claim 2, wherein said step of constraining includes
evaluating potential instances of said iterative solutions for a Bayesian
conditional probability therefor, the condition of said probability being
based on said final solution.

4. A method for use of EIT, comprising: performing an EIT procedure,
including distributing a plurality of electrodes on a subject's head such
that each electrode makes electrical contact with the head surface at a
respective location that is spaced apart from the location associated
with each other electrode, and performing an EIT current injecting step
of injecting a first set of currents into the head, each of the currents
of said first set being injected by a corresponding two or more of said
electrodes and having a sufficiently small magnitude so as not to provoke
a substantial brain response, either alone or in combination with other
currents of said first set, and, during a time when said two or more
electrodes is injecting one or more of the currents of said first set,
utilizing a plurality of the remaining electrodes to measure respective
first electrical potentials arising in response; performing an MREIT
procedure including an MREIT current injecting step that is the same as
said EIT current injecting step; obtaining a magnetic resonance image of
the currents injected according to said MREIT current injecting step; and
obtaining one or more iterative solutions to an EIT inverse problem posed
by said first electrical potentials in accordance with said first set of
currents, for specifying a distribution of static tissue impedances
within the head, including constraining one or more of said iterative
solutions by said magnetic resonance image.

5. The method of claim 4, wherein said step of constraining includes
evaluating potential instances of said iterative solutions for a Bayesian
conditional probability therefor, the condition of said probability being
based on said magnetic resonance image.

6. A method for use of EIT, comprising: performing an EIT procedure
including distributing a plurality of electrodes on a subject's head such
that each electrode makes electrical contact with the head surface at a
respective location that is spaced apart from the location associated
with each other electrode, and performing an EIT current injecting step
by injecting a first sets of currents into the head, each of the currents
of said first set being injected by a corresponding two or more of said
electrodes and having a sufficiently small magnitude so as not to provoke
a substantial brain response, either alone or in combination with other
currents of said first set, and, during time when said two or more
electrodes is injecting one or more of the currents of said first set,
utilizing a plurality of the remaining electrodes to measure respective
first electrical potentials arising in response; performing an MREIT
procedure including an MREIT current injecting step that is the same as
said EIT current injecting step; obtaining a magnetic resonance image of
the currents injected according to said MREIT current injecting step; and
obtaining one or more iterative solutions to an EIT inverse problem posed
by said first electrical potentials in accordance with said first set of
currents, for specifying the locations of tissue within the head
undergoing changes in impedance, including constraining one or more of
said iterative solutions by said magnetic resonance image.

7. The method of claim 6, wherein said step of constraining includes
evaluating potential instances of said iterative solutions for a Bayesian
conditional probability therefor, the condition of said probability being
based on said magnetic resonance image.

8. The method of claim 1, further comprising injecting one or more
stimulating currents into the head that either alone or in combination
are of sufficiently large magnitude to provoke a substantial brain
response, for stimulating said one or more sources if said EEG procedure
is performed, and for stimulating a second set of one or more sources,
which may be the same as said first set of sources, if said MREIT
procedure is performed, and monitoring one or more effects on the brain
of said stimulating currents utilizing either or both (A) said one or
more EEG solutions, if said EEG procedure is performed, and (B) said one
or more iterative EIT solutions according to at least one of steps (c)
and (d), if said MREIT procedure is performed.

9. The method of claim 8, further comprising modifying at least one of
the originating location, magnitude, and timing of at least one of said
stimulating currents based at least in part on feedback provided by said
step of monitoring.

10. The method of claim 9, wherein said stimulating currents are injected
contemporaneously with said first set of currents.

11. The method of claim 10, wherein said step of monitoring includes
observing the phase of electrical activity produced by at least one of
the sources of said first or second sets, and wherein said step of
modifying includes modifying said timing so as to shift said phase.

12. The method of claim 11, wherein if said MREIT procedure is performed,
said step of constraining includes evaluating potential instances of said
iterative solutions for a Bayesian conditional probability therefor, the
condition of said probability being based on said magnetic resonance
image.

13. The method of claim 10, wherein if said MREIT procedure is performed,
said step of constraining includes evaluating potential instances of said
iterative solutions for a Bayesian conditional probability therefor, the
condition of said probability being based on said magnetic resonance
image.

14. The method of claim 9, wherein if said MREIT procedure is performed,
said step of constraining includes evaluating potential instances of said
iterative solutions for a Bayesian conditional probability therefor, the
condition of said probability being based on said magnetic resonance
image.

15. The method of claim 8, wherein if said MREIT procedure is performed,
said step of constraining includes evaluating potential instances of said
iterative solutions for a Bayesian conditional probability therefor, the
condition of said probability being based on said magnetic resonance
image.

[0002] Electrical impedance tomography (hereinafter "EIT") is a known
technique for non-invasive spatial mapping of the electrical resistance
(referred to by use of the more general term "impedance") of internal
body tissues. The tissue impedance varies with tissue type and health,
and it also varies temporally, on the order of 10 milliseconds (ms) or
less, as a result of electrical activity occurring within the body
tissues. A particularly important source of electrical activity in the
body is the brain, and the present invention is particularly focused on
EIT used to map the impedance of tissues associated with the brain, e.g.,
cortex (white and gray matter), cerebrospinal fluid, skull, and scalp.

[0003] In ordinary EIT used as a tool for probing the brain, an array of
electrodes is applied to the head surface. Typically, the array consists
of 256 electrodes, and it is desirable to provide as many electrodes as
is practical, i.e., it is desirable to have a "dense" array.

[0004] Each electrode is used to "inject" an electrical current into the
head, i.e., into the tissues the impedances of which it is desired to
ascertain, and the remaining electrodes are used to measure the spatial
distribution of the resulting electrical potentials that arise at the
surface of the head.

[0005] Determining the internal tissue impedances responsible for the
measured potentials in view of the known injected currents is an example
of what is well known in the art as an "inverse problem." An inverse
problem is generally to deduce unknown structure in view of the
structure's known responses to known stimuli. To "solve" an inverse
problem is generally to hypothesize a mathematical model for the unknown
structure, test the model by applying the known stimuli mathematically to
determine whether its output agrees with those actually measured, assess
the error, adjust the model to try to reduce the error, and iterate these
steps until a convergence is obtained that represents an optimum
solution.

[0006] Inverse problems are generally "ill-posed," or ambiguous, so that
it is generally understood to be important to "constrain" the iterative
solution process by known relevant facts. One way of constraining the
solutions is to provide for greater resolution in the data, which is the
reason for preferring the dense array. Also, typically, for solving
inverse problems associated with probing internal anatomy such as the
brain, anatomical constraints are utilized, such as may be obtained by
magnetic resonance imaging (MRI).

[0007] EIT is used herein as a generic term that includes ordinary EIT as
well as magnetic resonance EIT, or "MREIT." MREIT is also a known
technique in which a magnetic resonance ("MR") image is obtained of the
injected currents, from which the current density can be determined,
which in turn allows for determining impedance.

[0008] MREIT does not require solving an inverse problem, and its spatial
resolution is superior to that of EIT. On the other hand, EIT does not
require use of an expensive MR imaging machine.

[0009] EIT has also been considered as a tool for imaging dynamic neural
functions in the brain. When neurons "fire" (i.e., depolarize), they
transfer ions into the extracellular space, decreasing their soma size
and cross-section for conducting current, decreasing their electrical
impedance. Conversely, when the neurons polarize they absorb ions from
the extracellular space, increasing their soma size and cross-section for
conducting current, increasing their electrical impedance. Once the
ordinary EIT inverse problem has been solved, i.e., once a satisfactory
model of the static impedances has been identified, the same model can in
principle be used to very quickly localize changes in impedance
associated with neural function. Unfortunately, the impedance changes are
too small to be reliably discerned, and EIT is not considered to be a
useful imaging modality.

[0010] Electroencephalography ("EEG") measures the electrical activity of
the brain. When neural activity (the ongoing synaptic effects) changes
the polarization of the soma of pyramidal neurons of the cortex (which
are aligned and therefore create far fields), there is a change in the
polarity between the soma at the apical dendrites (toward the surface of
the cortex). For example, greater negativity at the soma leads to
relative positivity at the apical dendrites, creating a dipole and thus a
dipolar field. These dipoles are referred to as "sources," and EEG is
used to localize the sources. In EEG, an array of electrodes is applied
to the head surface, and each electrode is used to measure electrical
potentials that arise at the surface of the head in response to source
activity.

[0011] EEG source localization also presents an inverse problem, so again,
it is particularly desirable to provide a dense array for EEG (dEEG). The
inverse problem is particularly to deduce the locations and relatives
strengths of the sources as would be needed to produce the measured
distribution of surface potentials, and it is solved in the same general
manner indicated above.

[0012] While EEG is a standard brain imaging tool, any tool that requires
solving an inverse problem will have limited capability. Recognizing
this, the present inventor developed the method described in U.S. Patent
Publication No. 20030093005, which combines images produced by EEG and
MRI to enhance resolution.

[0013] With the present invention, the inventor seeks to enhance the
capabilities of EIT as both a static tissue impedance modeling and
dynamic neural function imaging tool, as well as to employ EIT for
enhancing the capabilities of other modalities.

SUMMARY OF INVENTION

[0014] Methods for use of EIT are disclosed herein. The generic method has
four aspects.

[0015] All four aspects include performing an EIT procedure, which is
defined to include distributing a plurality of electrodes on a subject's
head such that each electrode makes electrical contact with the head
surface at a respective location that is spaced apart from the location
associated with each other electrode, and performing an EIT current
injecting step by injecting a first set of currents into the head, each
of the currents of the first set being injected by a corresponding two or
more of the electrodes and having a sufficiently small magnitude so as
not to provoke a substantial brain response, either alone or in
combination with other currents of the first set, and, during a time when
the first set of electrodes is injecting one or more of the currents of
the first set, utilizing a plurality of the remaining electrodes to
measure respective first electrical potentials arising in response.

[0016] At least one of an EEG procedure and an MREIT procedure, using the
same electrodes as used for performing the EIT procedure, is performed.

[0017] The EEG procedure is defined to include measuring, at the
aforementioned remaining electrodes, respective second electrical
potentials resulting from the electrical activity of one or more sources
in the brain during the same time that the at least two or more
electrodes are injecting a current. A final solution to an EIT inverse
problem for the distribution of static tissue impedances in the head
posed by the first injected currents and the first electrical potentials
is obtained. Also obtained are one or more iterative solutions to an EEG
inverse problem for localizing the sources posed by the second electrical
potentials. The EIT final solution is used as a constraint on one or more
of the EEG solutions.

[0018] The MREIT procedure is defined to include performing an MREIT
current injecting step that is the same as the EIT current injecting
step. Also, a magnetic resonance image of the currents injected in the
MREIT current injecting step is obtained, and the method includes either
or both the following steps: (c) obtaining one or more iterative EIT
solutions to an inverse problem for the distribution of static tissue
impedances in the head posed by the first injected currents and the first
electrical potentials, or (d) obtaining one or more iterative EIT
solutions to an inverse problem for localizing head tissues undergoing
changes in impedance which is also posed by the first injected currents
and the first electrical potentials. The solutions referred to as (c) and
(d) are constrained by the magnetic resonance image.

[0019] Preferably, the constraints provided by the magnetic resonance
image, if the MREIT procedure is performed, and by the EIT, if the EEG is
performed and the EIT is used as a constraint on solutions to the EEG
inverse problem, are computed or otherwise provided in the form of
Bayesian conditional probabilities.

[0020] In a second embodiment of the invention, any one or any combination
of the aforementioned methods is used as a monitor to provide feedback
for adjusting injected currents provided for the purpose of
neurostimulation. A particularly advantageous use of the second
embodiment is to adjust the phase of neural activity by adjusting the
timing of the stimulating currents in response to the feedback.

[0021] It is to be understood that this summary is provided as a means of
generally determining what follows in the drawings and detailed
description and is not intended to limit the scope of the invention.
Objects, features and advantages of the invention will be readily
understood upon consideration of the following detailed description taken
in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF DRAWINGS

[0022] FIG. 1 is a schematic/block diagram of a standard system for
performing EIT.

[0023] FIG. 2 is a flow diagram of a standard method for solving an
inverse problem.

[0024] FIG. 3 is a flow diagram of a methodology according to a second
aspect of the present invention, combining EIT used as a dynamic neural
function imaging modality with EEG.

[0025] FIG. 4 is a flow diagram of a methodology according to a third
aspect of the present invention, combining EIT used as a static tissue
impedance modeling modality with MREIT.

[0026] FIG. 5 is a flow diagram of a methodology according to a fourth
aspect of the present invention, combining EIT used as a dynamic neural
function imaging modality with MREIT.

DESCRIPTION OF PREFERRED EMBODIMENTS

First Embodiment

[0027] In a first embodiment of the invention, the invention utilizes EIT
in conjunction with either EEG and MREIT. In the case of the former, the
EIT is utilized as a dynamic neural function imaging tool, and in the
case of the latter, it is utilized either as a static tissue impedance
modeling tool or as a dynamic neural function imaging tool. Thus, the
invention can best be understood by considering each of the following
three aspects individually:

[0028] It is recognized for purposes herein that impedance has both static
and dynamic components. Static tissue impedance is that which
characterizes tissues that are either not associated with a source, are
if they are associated with a source, the source is not electrically
"active" in the sense of producing electrical currents (hereinafter
"neural currents").

[0029] Neural function is the result of the activity of active sources,
which cause changes in impedance in surrounding tissues. The
corresponding dynamic impedance components have periods on the order of
10 ms or less. "Dynamic neural function imaging" refers to mapping the
locations of tissues exhibiting dynamic impedance components.

[0030] In general, the invention is particularly adapted for use in
probing tissues internal to the head, with the objective being to probe
an electrically active brain; however, it is to be understood that
methods described herein may be utilized for probing any other internal
body tissue.

[0031] Aspect 1: EIT (Dynamic Neural Function Imaging)/EEG

[0032] According to the first aspect of the invention, EIT is used as an
imaging modality in conjunction with EEG, and will be referred to as a
"dynamic" EIT.

[0033] FIG. 1 shows a standard system 10 for performing an EIT generally,
which may be used in accordance with the present invention. The system
includes a sensor array 12 that is applied to the head 14 of a subject.
The array includes a number of electrodes 16 that are preferably held in
a controlled relationship to one another by a "sensor net" 18. A
preferred sensor net is described in the inventor's U.S. Pat. No.
5,291,888; however, the sensor array may make use of any desired method
or structure for applying the electrodes 16 to the head 14.

[0034] The electrodes are placed on the scalp so as to make as good an
electrical contact therewith as is practical. Typically and preferably
the electrodes are treated with a gel having an impedance that matches
the impedance of the scalp surface, so as to maximize the electrical
coupling between the scalp and the electrodes.

[0035] The electrodes 16 are individually controlled to act as current
sources for injecting predetermined currents into the head 14. More
particularly, a current source 22 is coupled to each of the electrodes 16
through respective, separate channels 24 (only three representative
channels 24 are shown, for simplicity).

[0036] A computer system 26 is used to instruct the current source 22 to
output, for each of the electrodes 16, a current that the computer system
determines. The computer system 26 may be an ordinary type, including a
CPU 26a, a memory 26b, and input and output ports 26c and 26d,
respectively. The current source is coupled to the computer through the
computer system's output port 26d.

[0037] Also coupled to each electrode 16 through respective separate
channels 28 is a voltage (potential) measuring apparatus 30 (only three
representative channels 28 are shown, for simplicity). The measuring
apparatus 30 is also coupled to the computer system's output port 26d,
and is adapted to measure the potentials at the electrodes on command of
the computer system 26.

[0038] The computer system 26 controls the current source 22 so that each
electrode 16, in turn, will inject a commanded current into the head 14,
and the computer system controls the voltage measuring apparatus 30 to
measure the voltage at each of the remaining electrodes. In addition, the
measuring apparatus is also coupled to the input port 26c of the computer
system, so that the measuring apparatus can report the measured
potentials back to the computer system.

[0039] Based on the known injected currents and the measured potentials,
the computer system 26 is adapted to compute the internal impedances that
are presumed to exist at various points on a single (planar) slice
through the head, by solving the aforementioned inverse problem for the
slice.

[0040] The descriptions hereinafter will not further distinguish between a
single slice and a volume comprising multiple slices, it being understood
that a volume can be built up of slices, and that, therefore, the same
principles will apply.

[0041] The details involved in solving the inverse problem are complex;
they are, however, well known in the art and need not be described for
purposes of understanding the invention. But it should be noted generally
that the inverse problem involves solving a series of different sets of
equations that relate input to output, where each set of equations
corresponds to a unique hypothetical model. Solving the inverse problem
involves solving one set of equations, corresponding to one model (e.g.,
an initial model), comparing the result with the real-world measurements,
noting the error, and adjusting (or re-adjusting) the model so as to
establish a revised set of equations, and so on.

[0042] So, referring to FIG. 2, there is in general, for solving any
inverse problem, a step 40 of establishing a set of equations
corresponding to a hypothesized model of the structure that it is desired
to discern. The model may be an active model, meaning that it includes
active elements, such as sources, or it may be a passive model, meaning
that it has only passive elements, such as impedances. The equations
define the output of the model, which in the case of a passive model is
responsive to an applied input, and in the case of an active model exists
even if there is no applied input. In the next step 42, this output is
computed using the model.

[0043] Having no particular timing relationship with steps 40 or 42, there
is also a step 41 of measuring the corresponding output of the real
structure. This step may be performed as many times as desired, but
typically it is performed only once.

[0044] In step 43, the output of step 42 is compared to the measured data,
and the (inevitable) error is identified in step 44. Then, in step 46 a
revised hypothesized model is selected, and in step 47 a set of equations
corresponding to the revised hypothesized model are established. After
this, step 42 is repeated; and steps 42-47 are repeated until the error
appears to be minimized, or is otherwise considered acceptable (step 45).

[0045] Using the system 10, potential measurements are made in response to
injected currents in the physical manner described above. However, it is
recognized that, as a result of source activity, there will be changes in
the measured potentials, due to changing impedances, when there are no
changes to the injected currents. So for imaging, potentials are measured
over time while holding the injected currents constant.

[0046] The dynamic EIT inverse problem is to deduce the locations of the
sources of changes in impedance that would be necessary to produce the
pattern of potential changes measured at the electrodes 16. As noted
above, the potential changes are small, rendering the inverse problem in
the case of EIT imaging to be extremely ill posed.

[0047] To improve the performance of EIT as a dynamic neural function
imaging method, the present invention provides for solving the dynamic
EIT inverse problem by utilizing an EEG "image," i.e., a source
localization, of the activity of the sources responsible for the dynamic
impedance.

[0048] Thus, an EEG procedure is also performed using the same system 10.
The current source 22 is not utilized; but the potential measuring
apparatus 30 may be utilized now to essentially continuously monitor the
electrical potentials at the sensors 16.

[0049] It is recognized as being important to use the same electrodes 16,
as they are installed for performing the dynamic EIT, to perform the EEG.
In that case, there is a "reciprocity," whereby the pathways taken by the
injected currents, and therefore characterized by the EIT, will be the
same paths taken by the neural currents characterized by the EEG. This is
recognized to provide for enhanced "convergence" of the solution to the
dynamic EEG inverse problem.

[0050] Hereinafter, it will be understood that reference herein to the
"same electrodes" does not necessarily mean that the electrodes are
physically the same--what is intended is to ensure that they are in the
same locations. This is most conveniently and preferably ensured by using
the same electrodes, and it is necessary that the electrodes be
physically the same as well as in the same locations when they are being
used for two purposes simultaneously.

[0051] It is the case here that the EIT and EEG measurements are to be
taken simultaneously, so that they are responsive to the same underlying
neurophysiological activity, i.e., the dipolar fields produced the
sources in the case of EEG and the associated ionic transfers between the
neurons and the neighboring extracellular spaces in the case of dynamic
EIT.

[0052] Based on the measured potentials, the computer system 26 is adapted
to solve an EEG inverse problem for the locations of the sources,
following the general methodology described above in connection with FIG.
2. The EEG source localization problem is well known and need be
described no further, except that it requires specification of static
internal tissue impedances, so a model of static tissue impedances is
required. Preferably, such a model is obtained by use of another EIT,
referred to herein as a "static" EIT.

[0053] It is recognized as being important to use the same electrodes 16,
as installed for performing the EEG, for performing the static EIT. In
that case, there is a "reciprocity," whereby the pathways taken by the
injected currents, and therefore characterized by the EIT, will be the
same paths taken by the neural currents characterized by the EEG. This is
recognized to provide for a an impedance model that is tailored for use
in solving the particular EEG inverse problem, as disclosed in the
inventor's U.S. Pat. No. 6,594,521.

[0054] There is no particular required relationship between the times the
static EIT and EEG procedures are conducted, since the static EIT is
being used to measure static tissue impedance.

[0055] It may be noted that the static EIT inverse problem is to deduce
the static impedance distribution that is necessary to transform the
known injected currents into the potentials measured at the electrodes
16. The below-described second aspect is directed to improving this
solution, and this improvement is preferably incorporated here.

[0056] It should also be noted that the EEG only "sees" source activity
that is coordinated with other source activity, i.e., a large number of
neurons must "fire" together to produce a measurable "far field" signal
at the electrodes 16. Otherwise, if the sources fire randomly, the fields
tend to cancel each other out in the aggregate. On the other hand, the
EIT is not measuring the fields produced by the sources, and therefore it
is not limited to "seeing" only impedance changes associated with neurons
that are firing together. Rather, the EIT will respond to impedance
changes whether as the result of coordinated neural activity or not. So
it is possible that the EIT will be responsive to neural activity that
the EEG cannot sense, and it might be expected that the EIT and EEG will
not provide complementary, or convergent, measures.

[0057] However, it is recognized that the EIT and EEG will be responsive
to the same neural activity if two conditions are met: (1) the activity
occurs in a particular part of the brain, such as the cortex, in which
the neurons are "aligned," and (2) the neurons are firing synchronously.
It is further recognized that these conditions can be met, based on the
following considerations.

[0058] First, the inverse problem is based on an assumed head model, and
the anatomical significance of the model locations of tissues undergoing
impedance changes is specified by the model. For example, the head model
will specify the volume of space assumed to be occupied by cortex. Any
proposed solution to the inverse problem for specifying the locations of
tissue undergoing impedance changes will specify locations for such
tissues. Whether those locations are in the volume of space assumed to be
occupied by cortex will be known. If they are, it can be assumed that the
measured impedance changes associated with those tissues will be
reflected in the EEG. Preferably, to improve the accuracy of such
assumptions, the head model is based on measured data, such as MRI data,
established for the particular head 14.

[0059] Second, while it remains that the neurons may not fire
synchronously, during any given short time period, if the neurons are
firing asynchronously, there will be fewer of them firing at any given
time than if they all fired together at that time. Thus, over a short
time interval, only a relatively small fraction of the neurons are
firing. This is important because the EIT (like EEG) has very good
temporal resolution, so it can discriminate between impedance changes
occurring during short time intervals and changes that occur during other
times. Thus, the EIT does not see a single impedance change resulting
from the aggregate of asynchronous neural firings over longer times;
instead, the EIT sees, instant-by-instant, a constant, relatively low
level of impedance change corresponding to the relatively small fraction
of neurons that fire within an instant.

[0060] In the context of the present invention, this is a useful
characteristic, because the EIT (again like EEG) also has very poor
signal to noise discrimination. Therefore, the EIT will not be sensitive
to these instant-by-instant impedance changes, due to their relatively
low magnitude.

[0061] So, it is recognized that the EIT is usefully non-responsive to
asynchronous neural activity, and so is usefully correlated with the EEG,
which is also not responsive to asynchronous neural activity, in areas of
the brain in which the neurons are aligned, such as the cortex.

[0062] The injected currents are of sufficiently small magnitude that they
do not provoke a brain response, typically about 10 milliamps. The
injected currents are also provided at a specific frequency, typically
200 Hz, and thus simply filtering the EEG measurements to exclude signal
at the frequencies of the injected currents is sufficient to ensure that
the EEG measurements are not contaminated by simultaneous current
injection.

[0063] Moreover, the injected currents are typically provided within a
narrow frequency range, such as +/-1 Hz, so that very little EEG signal
need be lost as a result of the filtering.

[0064] According to this second aspect of the invention, the EEG inverse
problem described above in connection with the first aspect is solved,
and the solution is used as a Bayesian constraint on the solution to the
EIT inverse problem.

[0065] The Bayesian constraints are conditional probabilities,
particularly considering here the likelihood that a given model of the
locations of tissues undergoing impedance changes should be chosen in
light of the source locations obtained by solving the EEG inverse
problem. For example, one may consider whether to place a source at a
given location in light of an indication obtained by solving the EEG
inverse problem that the source closest to that location is probably some
distance Δ away.

[0066] The EEG results are weighted, as probabilities, as desired to
influence to a desired degree of confidence the solution to the EIT
inverse problem. In the limit where the weighting factor for the EEG
results is "1," (100%) the EEG results would completely govern, and there
would be no advantage to combining with EIT. At the other extreme, if the
weighting factor for the EEG results is "0," the EIT results would
completely govern and there would be no advantage to combining with EEG.
The EIT is a particularly weak imaging method, so it is desirable to
weight the EEG results at greater than 50%; however, as noted above, the
EEG does not see all the impedance changes that the EIT will see, and
this is recognized to be a reason to discount the EEG results somewhat
more than they otherwise would be. Balancing these considerations, the
EEG results are preferably weighted in the range of 70-90%.

[0067] If the weighting factors are judiciously chosen, or if the actual
conditional probabilities are determined, EIT combined with EEG will
provide better spatial resolution than EEG alone.

[0068] The Bayesian constraints can be utilized at each iteration of steps
42-47, or they may be utilized less frequently, e.g., every other
iteration, or only when there is a relatively high degree of uncertainty.

[0069] With reference to FIG. 3, the methodology of the second aspect of
the invention is briefly reviewed as follows: In a step 100, an EIT
procedure is performed, and in a contemporaneous step 102, an EEG
procedure is performed on the head 14 using the same electrodes as used
in the EIT procedure. In a step 104 either contemporaneous with or
subsequent to step 100, a final solution to an EIT inverse problem
defining a final model of the location of tissue in the head 14 that
exhibited changes in impedance is obtained. In a step 106 subsequent to
steps 100-104, the EEG inverse problem is solved, using the final
solution to the EIT inverse problem from step 104 as a Bayesian
constraint.

[0070] Aspect 2: EIT (Static Tissue Impedance Modeling)/MREIT

[0071] According to the second aspect of the invention, static EIT is used
for static tissue impedance modeling, and the impedance model so obtained
is combined with another static impedance model obtained by use of MREIT.

[0072] The EIT static tissue impedance model is obtained as described
above in connection with the source localization procedure of the first
aspect of the invention. An MREIT impedance model is also obtained in the
standard manner, except that the MREIT and EIT procedures according to
the invention will utilize the same electrodes 16, as installed for
performing the EIT, to perform the MREIT. In that case, there is a
"reciprocity," whereby the pathways taken by the injected currents
characterized by the EIT will be the same paths taken by the injected
currents that are imaged in the MREIT. This is recognized to provide for
enhanced "convergence" of the solution to the EIT inverse problem.

[0073] There is no particular required relationship between the times the
EIT and MREIT procedures are conducted, since both the EIT and MREIT are
being used to measure static impedance.

[0074] The static EIT inverse problem is to deduce the static impedance
distribution that is necessary to transform the known injected currents
into the potentials measured at the electrodes 16. To improve the
performance of the EIT as a static tissue impedance modeling method, the
present invention provides for solving this EIT inverse problem by
utilizing the information available from the MREIT, as Bayesian
constraints.

[0075] Essentially, the Bayesian constraints here are to consider the
likelihood that a given model of the distribution of static impedance
should be chosen in light of the distribution indicated by the MREIT. For
example, one may consider whether to assign a static impedance value of X
to a given location in light of an indication from the MREIT that the
impedance value at that location is probably Y.

[0076] The MREIT results are weighted, as probabilities, as desired to
influence to a desired degree of confidence the solution to the EIT
inverse problem. In the limit where the weighting factor for the MREIT
results is "1," the MREIT results would completely govern, and there
would be no advantage to combining with EIT. At the other extreme, if the
weighting factor for the MREIT results is "0," the EIT results would
completely govern and there would be no advantage to combining with
MREIT. Since EIT and MREIT are comparably reliable modalities for static
tissue impedance modeling, the MREIT results should generally be weighted
at about 50% (e.g., in the range 45-55%); however, the balance can be
struck as desired or as needed depending on the particular case.

[0077] If the weighting factors are judiciously chosen, or if the actual
conditional probabilities are determined, combining EIT and MREIT will
provide better spatial resolution for the impedances than either EIT or
MREIT alone.

[0078] With reference to FIG. 4, the methodology of the third aspect of
the invention is reviewed as follows: In a step 200, an EIT procedure is
performed, and in another step 202 which bears no particular timing
relationship to step 200, an MREIT procedure is performed using the same
electrodes as used in the EIT procedure, to the point of obtaining a
magnetic resonance image. In a step 204 subsequent to step 202, an EIT
inverse problem defining a static tissue impedance model for the head 14
is solved, using the magnetic resonance image from step 202 as a Bayesian
constraint.

[0079] Aspect 3: EIT (Dynamic Neural Function Imaging)/MREIT

[0080] According to the third aspect of the invention, EIT is used for
dynamic neural function imaging, as in the first aspect of the invention.
However, in this case, the EIT inverse problem is constrained by MREIT,
which is obtained in the manner described immediately above in connection
with the second aspect of the invention. In particular, it is recognized
as being important to use the same electrodes 16, as they are installed
for performing the EIT, to perform the MREIT. In that case, there is a
"reciprocity," whereby the pathways taken by the injected currents, and
therefore characterized by the EIT, will be the same paths taken by the
neural currents characterized by the EEG. This is recognized to provide
for enhanced convergence of the solution to the dynamic EIT inverse
problem.

[0081] In this case, the EIT inverse problem is to deduce the locations of
the sources of changes in impedance that would be necessary to produce
the pattern of potential changes measured at the electrodes 16. To
improve the performance of the EIT as a dynamic neural function imaging
method, the present invention provides for solving this EIT inverse
problem by utilizing the information available from the MREIT,
particularly as Bayesian constraints.

[0082] Essentially, the Bayesian constraints here are to consider the
likelihood that a given model of the locations of impedance changes
should be chosen in light of the impedance distribution indicated by the
MREIT.

[0083] It is particularly important in this context to note that the MREIT
is substantially slower than EIT. Typically, the MREIT averages (or
integrates) over a time period of about 200 ms, and "fast" MREIT may
lower this period to 50 ms; whereas the changes in impedance to be
discerned are occurring within periods of 10 ms or less. On the other
hand, the MREIT has significantly better spatial resolution than EIT.

[0084] The present inventor has recognized that these two modalities are
particularly complementary, i.e., the MREIT has very good spatial
resolution but very poor temporal resolution; whereas the EIT has very
poor spatial resolution, but very good temporal resolution. The inventor
has also recognized that MREIT, though slow, is not so slow that it fails
completely to "see" the impedance changes that are seen by the EIT, i.e.,
both imaging modalities are recognized as being able to "see" the
impedance changes. Accordingly, utilizing the MREIT as a Bayesian
constraint on the solutions to the EIT inverse problem will result in an
image of dynamic neural function that has better temporal resolution than
that of the MREIT, and better spatial resolution than that of the EIT.

[0085] The weighting factors may be chosen to reflect the extent to which
improved spatial resolution is preferred over improved temporal
resolution, and vice versa. At a 50% weighting, both of these
characteristics will be about equally improved.

[0086] The EIT potential measurements are made simultaneously with the
MREIT, i.e., simultaneous with the magnetic resonance imaging and the
current injection occurring during the time required to acquire the
magnetic resonance image. This is accomplished by using the same current
injection step for both the EIT and MREIT, i.e., the same currents
applied at the same times at the same locations on the head surface are
used in both EIT procedures.

[0087] With reference to FIG. 6, the methodology of the fourth aspect of
the invention is briefly reviewed as follows: In a step 300, an EIT
procedure is performed, and in a contemporaneous step 302, an MREIT
procedure is performed on the head 14 using the same electrodes as used
in the EIT procedure, to the point of obtaining an MREIT impedance model.
In a step 304 either contemporaneous with or subsequent to step 300, an
EIT inverse problem defining the location of tissue in the head 14 that
exhibited changes in impedance is solved, using the MREIT impedance model
from step 302 as a Bayesian constraint.

Second Embodiment

[0088] In a second embodiment of the invention, the first embodiment is
utilized in conjunction with neuro stimulation, which for purposes herein
is stimulation of neural activity in the brain by the injection of
stimulating currents into the head. The same system described above for
performing EIT according to the first embodiment can be utilized for
neurostimulation. However, whereas the injected currents utilized in the
first embodiment can be on the order of 10-4 amps (100 microamps),
and are small enough not to provoke a discernible brain response, the
injected currents utilized in neurostimulation are two orders of
magnitude larger (about 10 milliamps) so that they will provoke a brain
response, which may persist for several seconds after their cessation.

[0089] The usual objective of neurostimulation is to stimulate the
aforementioned "sources" of neural activity in a subject's brain,
typically for the purpose of causing the sources to become active as they
would as a result of the subject's will, i.e., without the stimulation.
In such cases, the EEG potentials associated with the active sources will
be known, and the sources will have been localized, preferably according
to the first aspect of the first embodiment, in which an EIT static
impedance model of the head tissues is utilized in the solution to the
EEG inverse problem, utilizing the same electrodes for both the EIT and
EEG.

[0090] The EEG thus provides a model of where the sources are located, and
the static EIT provides a model from which the impedance from each
electrode to each of the sources can be determined. From these models,
the current that will flow to each of the sources as a result of a given
voltage applied to a given electrode can be computed in a standard
manner, and the current flows resulting from voltages applied to all the
other electrodes can be superimposed.

[0091] So far, injected stimulative currents have been applied according
to a predetermined plan, to provide a level of stimulation that is
intended to result in causing the sources to become active to
substantially the same extent that they were previously active on their
own. However, the plan will be subject to errors, so it is an outstanding
feature of the invention to monitor the stimulation to see whether the
effects are those that are desired. For example, monitoring may show
whether and how effectively the currents are flowing to the locations
that it is desired to stimulate, and monitoring may also show whether and
to what extent expected patterns of response for the type of activity
that it is desired to stimulate are actually occurring.

[0092] To monitor the currents, the currents could be imaged with MRI, and
to monitor the effects, EEG potential measurements could be used.
However, the present inventor has recognized that these monitors are
insufficient. Specifically, the MRI is insufficient because of its poor
temporal resolution, and the EEG is insufficient because of its poor
signal resolution. According to the present invention, these
insufficiencies are overcome by utilizing the first embodiment of the
invention as a monitor; particularly, any one or more of the
aforedescribed three aspects thereof.

[0093] So, according to the second embodiment of the invention, the first
embodiment of the invention is utilized to monitor the results of
neurostimulation, and the information provided by this monitoring is
utilized as feedback for adjusting the originating location (e.g., which
of the electrodes 16 is used), magnitude, and/or timing of the
stimulating currents, so that the stimulation is more effective to
produce the result sought.

[0094] It will be noted that EIT is utilized in all three aspects of the
first embodiment of the invention, and that EIT utilizes a relatively low
level current injection. So it will be appreciated that the second
embodiment of the invention utilizes two current injections, a relatively
high level current injection for stimulation and a relatively low level
current injection for impedance measurement. It is recognized by the
present inventor that these two current injections can be either
sequential or simultaneous.

[0095] Current injections are generally performed by selecting certain
electrodes 16 and applying to those electrodes a positive potential
relative to other selected electrodes 16, the former therefore being
sources of the injected current (hereinafter referred to as "injection
sources" to distinguish over the "sources" of neural activity), and the
latter being sinks for the injected current (hereinafter "injection
sinks"). The step of applying the potentials to a pair of electrodes will
be referred to herein as "activating" the electrodes of the pair.

[0096] Typically, though not necessarily, the EIT current injections (and
so too the MREIT current injections when EIT is combined with MREIT) are
performed by a "scanning" method of sequentially selecting and activating
various single pairs of the electrodes 16 to function as injection
sources and injection sinks. On the other hand, the neurostimulative
current injections are preferably and typically performed by selecting in
advance particular injection source/injection sink pairs and activating
the pairs simultaneously.

[0097] To reiterate, the relatively high level stimulative currents that
are employed for the purpose of stimulating source activity in the brain
may be injected either sequentially or simultaneously with the EIT (or
MREIT) relatively low level current injections that are employed for the
purpose of monitoring the stimulated activity (hereinafter "monitoring
currents").

[0098] Where the stimulative and monitoring currents are applied at
different times, the stimulative currents are applied first, and the
monitoring currents may be applied at any subsequent time at which the
sources remain, at least to a discernible extent, stimulated. It is has
been found that this can be up to several seconds after the stimulus has
been removed.

[0099] Where the stimulative and monitoring currents are applied at the
same time, there is no need to subtract the effects of the former from
measurements responsive to the latter. As noted above in connection with
the first embodiment, the EIT (or MREIT) injected currents are provided
within a narrow frequency range. Moreover, this frequency range is or can
be made to be relatively high as compared to the frequency of the brain
activity that is being stimulated. On the other hand, the stimulative
currents must be resonant with the frequency of the stimulated source
activity. So the EIT (or MREIT) currents are or can be made to be
independent of the stimulative currents, and therefore the two sets of
currents linearly superimpose. Therefore, if the monitor is to employ
either the second or third aspects of the first embodiment, no special
steps will be required to avoid contamination resulting from the
stimulation.

[0100] Also, because of the linear independence of the monitoring and
stimulating currents, it is recognized that the same electrodes can be
used simultaneously to inject both types of currents. This is also
recognized as being preferable.

[0101] The EEG, however, measures signal at the frequency of the
stimulating currents, so the EEG may be heavily contaminated by the
stimulation if appropriate ameliorative steps are not taken. As is
readily apparent, the EEG and EIT measures can be performed in sequence,
after the stimulation has ceased to avoid contamination. However,
preferably, the monitor is made available during the stimulation. The
present invention solves the contamination problem in this circumstance
by using the computer system 26 to compute the effect, at the location of
each of the monitoring electrodes 16, of the stimulating currents, using
a static EIT tissue impedance model, and subtracting or otherwise
mathematically accounting for this effect from the measured potentials at
the same electrodes.

[0102] The static EIT is performed as described above in connection with
the first embodiment. It is reiterated that there need be no particular
timing relationship between the static EIT for this purpose, and either
the stimulation or the EEG.

[0103] In the second embodiment where EIT and neurostimulation are
performed simultaneously, the invention can also be used for
synchronizing the source activity to the neurostimulative currents, and
thereby shifting the time of first occurrence of the source activity,
which will be referred to herein as shifting its "phase." The objective
in this case is not to stimulate activity that would not otherwise occur,
but to shift the phase of activity that is already occurring or will
otherwise occur.

[0104] Synchronization is the result of the stimulative currents changing
the polarization of the neural membranes, and thereby changing their
sensitivity to synaptic inputs. It is a known problem that it is
difficult to "focus" the stimulative currents on the particular neurons
of interest. However, in the case of neural activity that is already
occurring, or that is about to occur without stimulation, the neurons are
at or near the "threshold" for firing. In that case, it is recognized by
the present inventor that neurostimulative currents will preferentially
impact those particular neurons, and their effects on other areas of the
brain can be ignored.

[0105] As a rule, "neurons that fire together wire together," meaning that
when neurons fire in concert, their synaptic connectivities tend to be
recorded, or learned, by the brain, whereas if the neurons are out of
phase with one another, this learning will either not occur at all, or if
it does occur, it occurs less effectively. The present inventor has
recognized that synchronizing neural activity according to the invention
is therefore useful for either learning or unlearning particular patterns
of neural activity. Thus, it may be desirable to modify the timing of the
stimulating currents either to bring neural activity into better
synchronization or not. To achieve either result, it is important to be
able to "see" the source activity in real time, with
millisecond-by-millisecond resolution, to monitor the effect of the
neurostimulative currents on the phase of the activity, and the invention
provides this critical capability.

[0106] It is not considered important to the quality of the result to
utilize the same electrodes for the neurostimulation and the EIT;
however, this may be a practical advantage in some cases.

[0107] It is to be understood that, while specific uses of EIT have been
shown and described as preferred, other configurations and methods could
be utilized, in addition to those already mentioned, without departing
from the principles of the invention.

[0108] The terms and expressions which have been employed in the foregoing
specification are used therein as terms of description and not of
limitation, and there is no intention in the use of such terms and
expressions to exclude equivalents of the features shown and described or
portions thereof, it being recognized that the scope of the invention is
defined and limited only by the claims which follow.